Product Update: Help Isolate Whats Really Driving Interest With Trend Decomposition

Break broad trends into the exact brands, products, keywords, and conversations driving growth, share shifts, and momentum across social and web data.

Trend Decomposition breaks a broad keyword, company, category, or cultural theme into the specific products, brands, creators, hashtags, and related terms driving discussion share over time.

Instead of only seeing that demand is rising, users can now identify who is winning, what is gaining traction, and where momentum is coming from.

Why This Matters

Top-line trends are helpful.

But they often leave the most important questions unanswered:

• Is Nike being driven by Jordans, running shoes, or a celebrity collaboration?

• Is FoodTok being driven by Chick-fil-A, Wingstop, Crumbl, or a viral recipe?

• Is prebiotic soda growth coming from Olipop, Poppi, Bloom Nutrition, or a new entrant?

• Is AI conversation centered around chatbots, semiconductors, coding tools, or infrastructure?

Trend Decomposition answers those questions instantly.

How It Works

Users can open Trend Decomposition directly from the Social Summary section of any company or keyword inside TickerTrends.

Once launched, enter any trend or company and run a decomposition search.

Examples:

• Nike

• FoodTok

• Prebiotic soda

• Protein snacks

• Running shoes

• AI tools

TickerTrends then maps the terms most responsible for that conversation across time.

Partial Match vs Exact Match

Users can choose between two search modes.

Partial Match

Broader discovery mode that captures adjacent phrases and related discussions.

Great for finding new opportunities and uncovering emerging terms.

Exact Match

Cleaner precision mode focused only on posts including the base keyword.

Ideal for investment research and sharper competitive analysis.

Expanded Source Coverage

Trend Decomposition now supports multiple sources so users can understand where trends are forming.

Supported datasets may include:

• TikTok

• Instagram

• X

• Reddit

• LinkedIn

• YouTube

• Facebook

• Substack

• Threads

• Reels

Each platform tells a different story.

• TikTok often surfaces viral consumer products first

• Reddit reveals detailed consumer opinions

• LinkedIn highlights enterprise and professional trends

• X captures real-time reactions and narratives

  

Viral Timeline

At the center of Trend Decomposition is the Viral Timeline.

This view ranks the most influential related terms under each monthly period, allowing users to track how trends evolve.

Users can quickly identify:

• Which terms have the highest share of discussion

• Which names became recurring winners

• Which products spiked then faded

• Which brands are taking share now

Every term is color coded consistently across the timeline, making it easy to follow recurring winners over time.

Share Threshold Filters

Users can filter timeline visibility using share thresholds such as:

• 0.5%

• 1%

• 1.5%

• 2%

Lower thresholds reveal smaller niche contributors.

Higher thresholds isolate only the largest and most influential terms.

Word Filters

Users can also sort by structure:

• All Terms

• 1 Word

• 2+ Words

• Cashtags ($)

Examples:

For Nike:

• Jordan

• Dunk

• Air Jordan

• Running Shoes

• $NKE

This creates another layer of precision depending on use case.

Hover to See Trend Curves

Hovering or clicking any term opens a mini chart showing:

• Share of voice over time

• Peak period

• Momentum acceleration

• Current trend direction

This helps users quickly understand whether a term is rising, fading, or sustaining.

Compare Terms Mode

Users can select multiple terms and compare them directly.

Examples:

• Olipop vs Poppi

• Nike Dunk vs Adidas Samba

• Wingstop vs Chick-fil-A

• NVIDIA vs AMD

Comparison mode supports:

• Shared axis

• Separate axis

• Multiple time periods

• Direct share trend analysis

This is especially useful for market share battles and competitive tracking.

View Sample Posts

Trend Decomposition also surfaces the actual posts driving movement.

Instead of guessing why a product is trending, users can inspect the conversations responsible for the move.

This is valuable for understanding:

• Why a spike happened

• Which creator drove momentum

• Whether sentiment is positive or negative

• Which product feature is resonating most

  

Why It Matters for Investors

Most research tools tell you that something happened.

Trend Decomposition tells you:

• What caused it

• Who benefited

• Whether it is recurring

• If momentum is accelerating

• Which competitors are losing share

That difference matters when trying to identify winners early.

For access to our KPI forecasting platform, please contact admin@tickertrends.io .

TickerTrends Enterprise provides access to tracking and forecasting across 500+ public company KPIs, alongside actionable, data-driven research.
https://tickertrends.io/enterprise